Overview

Dataset statistics

Number of variables13
Number of observations1907
Missing cells31
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory193.8 KiB
Average record size in memory104.1 B

Variable types

Numeric9
Categorical4

Alerts

Game Title has a high cardinality: 1519 distinct valuesHigh cardinality
Publisher has a high cardinality: 94 distinct valuesHigh cardinality
df_index is highly overall correlated with Rank and 4 other fieldsHigh correlation
Rank is highly overall correlated with df_index and 4 other fieldsHigh correlation
Year is highly overall correlated with PlatformHigh correlation
North America is highly overall correlated with df_index and 2 other fieldsHigh correlation
Europe is highly overall correlated with df_index and 3 other fieldsHigh correlation
Rest of World is highly overall correlated with df_index and 3 other fieldsHigh correlation
Global is highly overall correlated with df_index and 4 other fieldsHigh correlation
Platform is highly overall correlated with YearHigh correlation
Year has 29 (1.5%) missing valuesMissing
df_index is uniformly distributedUniform
Rank is uniformly distributedUniform
Game Title is uniformly distributedUniform
df_index has unique valuesUnique
Rank has unique valuesUnique
North America has 61 (3.2%) zerosZeros
Europe has 74 (3.9%) zerosZeros
Japan has 813 (42.6%) zerosZeros
Rest of World has 61 (3.2%) zerosZeros

Reproduction

Analysis started2023-01-12 04:51:37.874420
Analysis finished2023-01-12 04:51:46.803686
Duration8.93 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct1907
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean953
Minimum0
Maximum1906
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-01-12T10:21:46.965685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile95.3
Q1476.5
median953
Q31429.5
95-th percentile1810.7
Maximum1906
Range1906
Interquartile range (IQR)953

Descriptive statistics

Standard deviation550.6478
Coefficient of variation (CV)0.57780462
Kurtosis-1.2
Mean953
Median Absolute Deviation (MAD)477
Skewness0
Sum1817371
Variance303213
MonotonicityStrictly increasing
2023-01-12T10:21:47.229734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
1267 1
 
0.1%
1279 1
 
0.1%
1278 1
 
0.1%
1277 1
 
0.1%
1276 1
 
0.1%
1275 1
 
0.1%
1274 1
 
0.1%
1273 1
 
0.1%
1272 1
 
0.1%
Other values (1897) 1897
99.5%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
1906 1
0.1%
1905 1
0.1%
1904 1
0.1%
1903 1
0.1%
1902 1
0.1%
1901 1
0.1%
1900 1
0.1%
1899 1
0.1%
1898 1
0.1%
1897 1
0.1%

Rank
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct1907
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean954
Minimum1
Maximum1907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-01-12T10:21:47.323155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.3
Q1477.5
median954
Q31430.5
95-th percentile1811.7
Maximum1907
Range1906
Interquartile range (IQR)953

Descriptive statistics

Standard deviation550.6478
Coefficient of variation (CV)0.57719895
Kurtosis-1.2
Mean954
Median Absolute Deviation (MAD)477
Skewness0
Sum1819278
Variance303213
MonotonicityStrictly increasing
2023-01-12T10:21:47.389779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1268 1
 
0.1%
1280 1
 
0.1%
1279 1
 
0.1%
1278 1
 
0.1%
1277 1
 
0.1%
1276 1
 
0.1%
1275 1
 
0.1%
1274 1
 
0.1%
1273 1
 
0.1%
Other values (1897) 1897
99.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1907 1
0.1%
1906 1
0.1%
1905 1
0.1%
1904 1
0.1%
1903 1
0.1%
1902 1
0.1%
1901 1
0.1%
1900 1
0.1%
1899 1
0.1%
1898 1
0.1%

Game Title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1519
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
LEGO Batman: The Videogame
 
6
FIFA Soccer 08
 
6
LEGO Indiana Jones: The Original Adventures
 
6
WWE SmackDown vs Raw 2008
 
5
Pro Evolution Soccer 2008
 
5
Other values (1514)
1879 

Length

Max length102
Median length49
Mean length21.009963
Min length3

Characters and Unicode

Total characters40066
Distinct characters80
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1242 ?
Unique (%)65.1%

Sample

1st rowWii Sports
2nd rowSuper Mario Bros.
3rd rowMario Kart Wii
4th rowWii Sports Resort
5th rowTetris

Common Values

ValueCountFrequency (%)
LEGO Batman: The Videogame 6
 
0.3%
FIFA Soccer 08 6
 
0.3%
LEGO Indiana Jones: The Original Adventures 6
 
0.3%
WWE SmackDown vs Raw 2008 5
 
0.3%
Pro Evolution Soccer 2008 5
 
0.3%
Star Wars: The Force Unleashed 5
 
0.3%
The Simpsons Game 5
 
0.3%
FIFA Soccer 10 5
 
0.3%
Guitar Hero III: Legends of Rock 4
 
0.2%
LEGO Star Wars II: The Original Trilogy 4
 
0.2%
Other values (1509) 1856
97.3%

Length

2023-01-12T10:21:47.507673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 300
 
4.3%
of 204
 
2.9%
2 182
 
2.6%
3 101
 
1.4%
mario 80
 
1.1%
68
 
1.0%
soccer 58
 
0.8%
star 56
 
0.8%
ii 56
 
0.8%
super 55
 
0.8%
Other values (1652) 5844
83.4%

Most occurring characters

ValueCountFrequency (%)
5097
 
12.7%
e 3103
 
7.7%
o 2488
 
6.2%
a 2471
 
6.2%
r 2246
 
5.6%
n 1969
 
4.9%
i 1859
 
4.6%
t 1708
 
4.3%
s 1489
 
3.7%
l 1182
 
3.0%
Other values (70) 16454
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25587
63.9%
Uppercase Letter 7076
 
17.7%
Space Separator 5097
 
12.7%
Decimal Number 1218
 
3.0%
Other Punctuation 970
 
2.4%
Dash Punctuation 81
 
0.2%
Open Punctuation 17
 
< 0.1%
Close Punctuation 17
 
< 0.1%
Currency Symbol 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3103
12.1%
o 2488
9.7%
a 2471
9.7%
r 2246
 
8.8%
n 1969
 
7.7%
i 1859
 
7.3%
t 1708
 
6.7%
s 1489
 
5.8%
l 1182
 
4.6%
d 1012
 
4.0%
Other values (17) 6060
23.7%
Uppercase Letter
ValueCountFrequency (%)
S 823
 
11.6%
T 541
 
7.6%
M 477
 
6.7%
C 415
 
5.9%
A 394
 
5.6%
F 392
 
5.5%
D 380
 
5.4%
R 362
 
5.1%
I 357
 
5.0%
W 348
 
4.9%
Other values (16) 2587
36.6%
Decimal Number
ValueCountFrequency (%)
2 359
29.5%
0 332
27.3%
3 133
 
10.9%
1 108
 
8.9%
4 75
 
6.2%
9 63
 
5.2%
8 41
 
3.4%
5 39
 
3.2%
7 34
 
2.8%
6 34
 
2.8%
Other Punctuation
ValueCountFrequency (%)
: 631
65.1%
' 143
 
14.7%
. 79
 
8.1%
! 47
 
4.8%
& 41
 
4.2%
/ 25
 
2.6%
, 3
 
0.3%
* 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 15
88.2%
[ 2
 
11.8%
Close Punctuation
ValueCountFrequency (%)
) 15
88.2%
] 2
 
11.8%
Space Separator
ValueCountFrequency (%)
5097
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32663
81.5%
Common 7403
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3103
 
9.5%
o 2488
 
7.6%
a 2471
 
7.6%
r 2246
 
6.9%
n 1969
 
6.0%
i 1859
 
5.7%
t 1708
 
5.2%
s 1489
 
4.6%
l 1182
 
3.6%
d 1012
 
3.1%
Other values (43) 13136
40.2%
Common
ValueCountFrequency (%)
5097
68.9%
: 631
 
8.5%
2 359
 
4.8%
0 332
 
4.5%
' 143
 
1.9%
3 133
 
1.8%
1 108
 
1.5%
- 81
 
1.1%
. 79
 
1.1%
4 75
 
1.0%
Other values (17) 365
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40036
99.9%
None 30
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5097
 
12.7%
e 3103
 
7.8%
o 2488
 
6.2%
a 2471
 
6.2%
r 2246
 
5.6%
n 1969
 
4.9%
i 1859
 
4.6%
t 1708
 
4.3%
s 1489
 
3.7%
l 1182
 
3.0%
Other values (68) 16424
41.0%
None
ValueCountFrequency (%)
é 29
96.7%
° 1
 
3.3%

Platform
Categorical

Distinct22
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
PS2
372 
PS
223 
X360
219 
PS3
202 
Wii
161 
Other values (17)
730 

Length

Max length4
Median length3
Mean length2.810173
Min length2

Characters and Unicode

Total characters5359
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowWii
2nd rowNES
3rd rowWii
4th rowWii
5th rowGB

Common Values

ValueCountFrequency (%)
PS2 372
19.5%
PS 223
11.7%
X360 219
11.5%
PS3 202
10.6%
Wii 161
8.4%
DS 149
7.8%
GBA 75
 
3.9%
XB 72
 
3.8%
PC 71
 
3.7%
PSP 63
 
3.3%
Other values (12) 300
15.7%

Length

2023-01-12T10:21:47.589801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ps2 372
19.5%
ps 223
11.7%
x360 219
11.5%
ps3 202
10.6%
wii 161
8.4%
ds 149
7.8%
gba 75
 
3.9%
xb 72
 
3.8%
pc 71
 
3.7%
psp 63
 
3.3%
Other values (12) 300
15.7%

Most occurring characters

ValueCountFrequency (%)
S 1171
21.9%
P 995
18.6%
3 436
 
8.1%
2 372
 
6.9%
i 326
 
6.1%
X 291
 
5.4%
6 276
 
5.2%
0 219
 
4.1%
B 195
 
3.6%
G 189
 
3.5%
Other values (10) 889
16.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3673
68.5%
Decimal Number 1360
 
25.4%
Lowercase Letter 326
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 1171
31.9%
P 995
27.1%
X 291
 
7.9%
B 195
 
5.3%
G 189
 
5.1%
D 171
 
4.7%
N 169
 
4.6%
W 163
 
4.4%
C 133
 
3.6%
E 112
 
3.0%
Other values (4) 84
 
2.3%
Decimal Number
ValueCountFrequency (%)
3 436
32.1%
2 372
27.4%
6 276
20.3%
0 219
16.1%
4 57
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
i 326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3999
74.6%
Common 1360
 
25.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1171
29.3%
P 995
24.9%
i 326
 
8.2%
X 291
 
7.3%
B 195
 
4.9%
G 189
 
4.7%
D 171
 
4.3%
N 169
 
4.2%
W 163
 
4.1%
C 133
 
3.3%
Other values (5) 196
 
4.9%
Common
ValueCountFrequency (%)
3 436
32.1%
2 372
27.4%
6 276
20.3%
0 219
16.1%
4 57
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1171
21.9%
P 995
18.6%
3 436
 
8.1%
2 372
 
6.9%
i 326
 
6.1%
X 291
 
5.4%
6 276
 
5.2%
0 219
 
4.1%
B 195
 
3.6%
G 189
 
3.5%
Other values (10) 889
16.6%

Year
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)1.6%
Missing29
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2003.7668
Minimum1983
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-01-12T10:21:47.657214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1992
Q12000
median2005
Q32008
95-th percentile2011
Maximum2012
Range29
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.8953693
Coefficient of variation (CV)0.0029421435
Kurtosis0.94034931
Mean2003.7668
Median Absolute Deviation (MAD)4
Skewness-1.013926
Sum3763074
Variance34.755379
MonotonicityNot monotonic
2023-01-12T10:21:47.722167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2008 184
 
9.6%
2007 157
 
8.2%
2009 131
 
6.9%
2010 130
 
6.8%
2004 122
 
6.4%
2003 114
 
6.0%
2002 110
 
5.8%
2005 105
 
5.5%
2006 103
 
5.4%
2011 100
 
5.2%
Other values (20) 622
32.6%
ValueCountFrequency (%)
1983 6
 
0.3%
1984 9
0.5%
1985 6
 
0.3%
1986 12
0.6%
1987 7
 
0.4%
1988 9
0.5%
1989 9
0.5%
1990 13
0.7%
1991 10
0.5%
1992 20
1.0%
ValueCountFrequency (%)
2012 60
 
3.1%
2011 100
5.2%
2010 130
6.8%
2009 131
6.9%
2008 184
9.6%
2007 157
8.2%
2006 103
5.4%
2005 105
5.5%
2004 122
6.4%
2003 114
6.0%

Genre
Categorical

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
Sports
308 
Action
275 
Shooter
206 
Platform
188 
Racing
186 
Other values (7)
744 

Length

Max length12
Median length10
Mean length7.2228631
Min length4

Characters and Unicode

Total characters13774
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSports
2nd rowPlatform
3rd rowRacing
4th rowSports
5th rowPuzzle

Common Values

ValueCountFrequency (%)
Sports 308
16.2%
Action 275
14.4%
Shooter 206
10.8%
Platform 188
9.9%
Racing 186
9.8%
Role-Playing 173
9.1%
Misc 159
8.3%
Fighting 126
6.6%
Adventure 110
 
5.8%
Simulation 92
 
4.8%
Other values (2) 84
 
4.4%

Length

2023-01-12T10:21:47.805263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sports 308
16.2%
action 275
14.4%
shooter 206
10.8%
platform 188
9.9%
racing 186
9.8%
role-playing 173
9.1%
misc 159
8.3%
fighting 126
6.6%
adventure 110
 
5.8%
simulation 92
 
4.8%
Other values (2) 84
 
4.4%

Most occurring characters

ValueCountFrequency (%)
o 1448
 
10.5%
t 1385
 
10.1%
i 1229
 
8.9%
n 962
 
7.0%
r 852
 
6.2%
e 683
 
5.0%
a 679
 
4.9%
l 670
 
4.9%
g 651
 
4.7%
S 646
 
4.7%
Other values (17) 4569
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11521
83.6%
Uppercase Letter 2080
 
15.1%
Dash Punctuation 173
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1448
12.6%
t 1385
12.0%
i 1229
10.7%
n 962
8.3%
r 852
 
7.4%
e 683
 
5.9%
a 679
 
5.9%
l 670
 
5.8%
g 651
 
5.7%
c 620
 
5.4%
Other values (10) 2342
20.3%
Uppercase Letter
ValueCountFrequency (%)
S 646
31.1%
P 405
19.5%
A 385
18.5%
R 359
17.3%
M 159
 
7.6%
F 126
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13601
98.7%
Common 173
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1448
 
10.6%
t 1385
 
10.2%
i 1229
 
9.0%
n 962
 
7.1%
r 852
 
6.3%
e 683
 
5.0%
a 679
 
5.0%
l 670
 
4.9%
g 651
 
4.8%
S 646
 
4.7%
Other values (16) 4396
32.3%
Common
ValueCountFrequency (%)
- 173
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1448
 
10.5%
t 1385
 
10.1%
i 1229
 
8.9%
n 962
 
7.0%
r 852
 
6.2%
e 683
 
5.0%
a 679
 
4.9%
l 670
 
4.9%
g 651
 
4.7%
S 646
 
4.7%
Other values (17) 4569
33.2%

Publisher
Categorical

Distinct94
Distinct (%)4.9%
Missing2
Missing (%)0.1%
Memory size15.0 KiB
Electronic Arts
341 
Nintendo
296 
Sony Computer Entertainment
156 
Activision
141 
Ubisoft
93 
Other values (89)
878 

Length

Max length38
Median length26
Mean length13.366929
Min length3

Characters and Unicode

Total characters25464
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)2.2%

Sample

1st rowNintendo
2nd rowNintendo
3rd rowNintendo
4th rowNintendo
5th rowNintendo

Common Values

ValueCountFrequency (%)
Electronic Arts 341
17.9%
Nintendo 296
15.5%
Sony Computer Entertainment 156
 
8.2%
Activision 141
 
7.4%
Ubisoft 93
 
4.9%
THQ 89
 
4.7%
Sega 81
 
4.2%
Take-Two Interactive 75
 
3.9%
Capcom 63
 
3.3%
Konami Digital Entertainment 53
 
2.8%
Other values (84) 517
27.1%

Length

2023-01-12T10:21:47.905191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
electronic 341
 
10.4%
arts 341
 
10.4%
nintendo 296
 
9.1%
entertainment 261
 
8.0%
interactive 182
 
5.6%
sony 157
 
4.8%
computer 156
 
4.8%
activision 141
 
4.3%
ubisoft 93
 
2.8%
thq 89
 
2.7%
Other values (111) 1213
37.1%

Most occurring characters

ValueCountFrequency (%)
t 2847
 
11.2%
n 2477
 
9.7%
e 2229
 
8.8%
i 2173
 
8.5%
o 1791
 
7.0%
r 1609
 
6.3%
1365
 
5.4%
a 1295
 
5.1%
c 1262
 
5.0%
s 1028
 
4.0%
Other values (46) 7388
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20366
80.0%
Uppercase Letter 3594
 
14.1%
Space Separator 1365
 
5.4%
Dash Punctuation 75
 
0.3%
Decimal Number 45
 
0.2%
Other Punctuation 19
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2847
14.0%
n 2477
12.2%
e 2229
10.9%
i 2173
10.7%
o 1791
8.8%
r 1609
7.9%
a 1295
6.4%
c 1262
6.2%
s 1028
 
5.0%
m 764
 
3.8%
Other values (15) 2891
14.2%
Uppercase Letter
ValueCountFrequency (%)
E 674
18.8%
A 571
15.9%
S 411
11.4%
N 338
9.4%
T 269
 
7.5%
C 259
 
7.2%
I 189
 
5.3%
G 144
 
4.0%
U 111
 
3.1%
H 103
 
2.9%
Other values (13) 525
14.6%
Decimal Number
ValueCountFrequency (%)
5 16
35.6%
9 12
26.7%
0 6
 
13.3%
8 6
 
13.3%
3 5
 
11.1%
Space Separator
ValueCountFrequency (%)
1365
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23960
94.1%
Common 1504
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2847
11.9%
n 2477
 
10.3%
e 2229
 
9.3%
i 2173
 
9.1%
o 1791
 
7.5%
r 1609
 
6.7%
a 1295
 
5.4%
c 1262
 
5.3%
s 1028
 
4.3%
m 764
 
3.2%
Other values (38) 6485
27.1%
Common
ValueCountFrequency (%)
1365
90.8%
- 75
 
5.0%
. 19
 
1.3%
5 16
 
1.1%
9 12
 
0.8%
0 6
 
0.4%
8 6
 
0.4%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2847
 
11.2%
n 2477
 
9.7%
e 2229
 
8.8%
i 2173
 
8.5%
o 1791
 
7.0%
r 1609
 
6.3%
1365
 
5.4%
a 1295
 
5.1%
c 1262
 
5.0%
s 1028
 
4.0%
Other values (46) 7388
29.0%

North America
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct375
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2587887
Minimum0
Maximum40.43
Zeros61
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-01-12T10:21:48.030865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.51
median0.81
Q31.375
95-th percentile3.394
Maximum40.43
Range40.43
Interquartile range (IQR)0.865

Descriptive statistics

Standard deviation1.9565601
Coefficient of variation (CV)1.5543197
Kurtosis135.52749
Mean1.2587887
Median Absolute Deviation (MAD)0.37
Skewness9.3182582
Sum2400.51
Variance3.8281272
MonotonicityNot monotonic
2023-01-12T10:21:48.123050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
 
3.2%
0.52 26
 
1.4%
0.5 25
 
1.3%
0.72 23
 
1.2%
0.68 22
 
1.2%
0.44 22
 
1.2%
0.56 21
 
1.1%
0.85 21
 
1.1%
0.78 21
 
1.1%
0.45 20
 
1.0%
Other values (365) 1645
86.3%
ValueCountFrequency (%)
0 61
3.2%
0.01 4
 
0.2%
0.02 3
 
0.2%
0.03 1
 
0.1%
0.04 2
 
0.1%
0.05 3
 
0.2%
0.06 2
 
0.1%
0.07 2
 
0.1%
0.08 4
 
0.2%
0.09 6
 
0.3%
ValueCountFrequency (%)
40.43 1
0.1%
29.08 1
0.1%
26.93 1
0.1%
23.2 1
0.1%
14.82 1
0.1%
14.5 1
0.1%
13.83 1
0.1%
13.5 1
0.1%
13.35 1
0.1%
12.78 1
0.1%

Europe
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct273
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70667541
Minimum0
Maximum28.39
Zeros74
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-01-12T10:21:48.233244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.23
median0.44
Q30.81
95-th percentile2.147
Maximum28.39
Range28.39
Interquartile range (IQR)0.58

Descriptive statistics

Standard deviation1.148904
Coefficient of variation (CV)1.6257874
Kurtosis195.66535
Mean0.70667541
Median Absolute Deviation (MAD)0.26
Skewness10.360124
Sum1347.63
Variance1.3199803
MonotonicityNot monotonic
2023-01-12T10:21:48.338888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
3.9%
0.38 42
 
2.2%
0.35 36
 
1.9%
0.36 31
 
1.6%
0.32 30
 
1.6%
0.03 30
 
1.6%
0.12 28
 
1.5%
0.44 27
 
1.4%
0.16 27
 
1.4%
0.01 26
 
1.4%
Other values (263) 1556
81.6%
ValueCountFrequency (%)
0 74
3.9%
0.01 26
 
1.4%
0.02 24
 
1.3%
0.03 30
1.6%
0.04 24
 
1.3%
0.05 23
 
1.2%
0.06 8
 
0.4%
0.07 13
 
0.7%
0.08 10
 
0.5%
0.09 8
 
0.4%
ValueCountFrequency (%)
28.39 1
0.1%
12.22 1
0.1%
10.81 1
0.1%
10.51 1
0.1%
9.11 1
0.1%
9.1 1
0.1%
8.87 1
0.1%
8.48 1
0.1%
8.15 1
0.1%
7.94 1
0.1%

Japan
Real number (ℝ)

Distinct218
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31749345
Minimum0
Maximum7.2
Zeros813
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-01-12T10:21:48.423645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.3
95-th percentile1.52
Maximum7.2
Range7.2
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.72494528
Coefficient of variation (CV)2.2833394
Kurtosis23.600869
Mean0.31749345
Median Absolute Deviation (MAD)0.02
Skewness4.2290219
Sum605.46
Variance0.52554566
MonotonicityNot monotonic
2023-01-12T10:21:48.539320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 813
42.6%
0.01 107
 
5.6%
0.02 85
 
4.5%
0.03 59
 
3.1%
0.04 52
 
2.7%
0.05 43
 
2.3%
0.06 29
 
1.5%
0.08 25
 
1.3%
0.09 21
 
1.1%
0.07 20
 
1.0%
Other values (208) 653
34.2%
ValueCountFrequency (%)
0 813
42.6%
0.01 107
 
5.6%
0.02 85
 
4.5%
0.03 59
 
3.1%
0.04 52
 
2.7%
0.05 43
 
2.3%
0.06 29
 
1.5%
0.07 20
 
1.0%
0.08 25
 
1.3%
0.09 21
 
1.1%
ValueCountFrequency (%)
7.2 1
0.1%
6.81 1
0.1%
6.48 1
0.1%
6.04 1
0.1%
5.64 1
0.1%
5.38 1
0.1%
5.33 1
0.1%
5.32 1
0.1%
4.66 1
0.1%
4.35 1
0.1%

Rest of World
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2064709
Minimum0
Maximum8.54
Zeros61
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-01-12T10:21:48.635851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.06
median0.13
Q30.22
95-th percentile0.667
Maximum8.54
Range8.54
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.34309326
Coefficient of variation (CV)1.6617028
Kurtosis197.18246
Mean0.2064709
Median Absolute Deviation (MAD)0.07
Skewness10.259001
Sum393.74
Variance0.11771298
MonotonicityNot monotonic
2023-01-12T10:21:48.705404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07 107
 
5.6%
0.06 89
 
4.7%
0.04 87
 
4.6%
0.02 85
 
4.5%
0.13 83
 
4.4%
0.09 82
 
4.3%
0.08 72
 
3.8%
0.03 70
 
3.7%
0.1 68
 
3.6%
0.11 68
 
3.6%
Other values (119) 1096
57.5%
ValueCountFrequency (%)
0 61
3.2%
0.01 36
 
1.9%
0.02 85
4.5%
0.03 70
3.7%
0.04 87
4.6%
0.05 55
2.9%
0.06 89
4.7%
0.07 107
5.6%
0.08 72
3.8%
0.09 82
4.3%
ValueCountFrequency (%)
8.54 1
0.1%
3.21 1
0.1%
3.01 1
0.1%
2.88 1
0.1%
2.84 1
0.1%
2.73 1
0.1%
2.49 1
0.1%
2.25 1
0.1%
2.08 1
0.1%
2.04 1
0.1%

Global
Real number (ℝ)

Distinct479
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4892396
Minimum0.83
Maximum81.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-01-12T10:21:48.807346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.83
5-th percentile0.88
Q11.11
median1.53
Q32.54
95-th percentile6.362
Maximum81.12
Range80.29
Interquartile range (IQR)1.43

Descriptive statistics

Standard deviation3.5631594
Coefficient of variation (CV)1.4314248
Kurtosis147.48456
Mean2.4892396
Median Absolute Deviation (MAD)0.53
Skewness9.2711026
Sum4746.98
Variance12.696105
MonotonicityDecreasing
2023-01-12T10:21:48.886093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27
 
1.4%
0.84 25
 
1.3%
1.14 24
 
1.3%
0.9 23
 
1.2%
0.94 23
 
1.2%
0.97 22
 
1.2%
1.04 21
 
1.1%
1.25 21
 
1.1%
0.91 21
 
1.1%
0.87 20
 
1.0%
Other values (469) 1680
88.1%
ValueCountFrequency (%)
0.83 11
0.6%
0.84 25
1.3%
0.85 19
1.0%
0.86 17
0.9%
0.87 20
1.0%
0.88 13
0.7%
0.89 19
1.0%
0.9 23
1.2%
0.91 21
1.1%
0.92 18
0.9%
ValueCountFrequency (%)
81.12 1
0.1%
40.24 1
0.1%
33.55 1
0.1%
31.52 1
0.1%
30.26 1
0.1%
29.08 1
0.1%
28.71 1
0.1%
28.31 1
0.1%
26.75 1
0.1%
24.5 1
0.1%

Review
Real number (ℝ)

Distinct734
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.038977
Minimum30.5
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-01-12T10:21:49.007403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30.5
5-th percentile56.406
Q174
median81
Q386.23
95-th percentile92
Maximum97
Range66.5
Interquartile range (IQR)12.23

Descriptive statistics

Standard deviation10.616899
Coefficient of variation (CV)0.13432486
Kurtosis1.9255471
Mean79.038977
Median Absolute Deviation (MAD)6
Skewness-1.2650859
Sum150727.33
Variance112.71855
MonotonicityNot monotonic
2023-01-12T10:21:49.095526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 63
 
3.3%
85 61
 
3.2%
82 60
 
3.1%
81 60
 
3.1%
86 56
 
2.9%
78 51
 
2.7%
83 51
 
2.7%
87 48
 
2.5%
80 44
 
2.3%
88 43
 
2.3%
Other values (724) 1370
71.8%
ValueCountFrequency (%)
30.5 1
0.1%
31 1
0.1%
33 1
0.1%
34 1
0.1%
35 1
0.1%
36 1
0.1%
37 1
0.1%
37.58 1
0.1%
38.15 1
0.1%
39 1
0.1%
ValueCountFrequency (%)
97 1
0.1%
96.36 1
0.1%
96.35 1
0.1%
96.3 1
0.1%
96.12 1
0.1%
96.09 1
0.1%
95.83 1
0.1%
95.77 1
0.1%
95.69 1
0.1%
95.54 1
0.1%

Interactions

2023-01-12T10:21:45.639009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:38.955561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.791466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.697636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.468906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.438865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.220670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.117975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.859216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.724507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.068372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.886995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.794418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.553174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.518889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.435721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.203224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.954599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.818260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.157381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.978004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.875575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.660779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.609074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.529984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.295024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.046823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.906850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.241038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.065536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.954954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.775069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.695728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.607865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.376690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.118680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.997506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.337259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.270918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.036559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.935111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.773970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.696828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.455879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.212989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:46.078014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.413881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.351585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.113693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.053355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.873752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.770820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.536895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.292628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:46.163384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.512222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.435647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.200727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.142686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.963816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.858822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.605113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.383446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:46.250302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.601886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.517196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.278056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.234094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.048497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.942364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.695012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.463559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:46.328047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:39.696534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:40.610705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:41.371293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:42.351704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:43.135844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.036923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:44.774395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-12T10:21:45.552970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-12T10:21:49.173914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
df_indexRankYearNorth AmericaEuropeJapanRest of WorldGlobalReviewPlatformGenrePublisher
df_index1.0001.0000.086-0.743-0.621-0.386-0.541-1.000-0.3310.0760.0330.124
Rank1.0001.0000.086-0.743-0.621-0.386-0.541-1.000-0.3310.0760.0330.124
Year0.0860.0861.000-0.0710.145-0.1670.432-0.086-0.0030.5750.1170.243
North America-0.743-0.743-0.0711.0000.3520.0390.3540.7430.2780.0570.0580.000
Europe-0.621-0.6210.1450.3521.0000.0870.7180.6210.1800.0000.0000.000
Japan-0.386-0.386-0.1670.0390.0871.0000.0240.3860.2560.1630.1010.095
Rest of World-0.541-0.5410.4320.3540.7180.0241.0000.5410.1840.0000.0000.000
Global-1.000-1.000-0.0860.7430.6210.3860.5411.0000.3310.0000.0270.000
Review-0.331-0.331-0.0030.2780.1800.2560.1840.3311.0000.1140.1070.298
Platform0.0760.0760.5750.0570.0000.1630.0000.0000.1141.0000.2100.223
Genre0.0330.0330.1170.0580.0000.1010.0000.0270.1070.2101.0000.320
Publisher0.1240.1240.2430.0000.0000.0950.0000.0000.2980.2230.3201.000

Missing values

2023-01-12T10:21:46.471625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-12T10:21:46.614402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-12T10:21:46.748247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

df_indexRankGame TitlePlatformYearGenrePublisherNorth AmericaEuropeJapanRest of WorldGlobalReview
001Wii SportsWii2006.0SportsNintendo40.4328.393.778.5481.1276.28
112Super Mario Bros.NES1985.0PlatformNintendo29.083.586.810.7740.2491.00
223Mario Kart WiiWii2008.0RacingNintendo14.5012.223.633.2133.5582.07
334Wii Sports ResortWii2009.0SportsNintendo14.8210.513.183.0131.5282.65
445TetrisGB1989.0PuzzleNintendo23.202.264.220.5830.2688.00
556New Super Mario Bros.DS2006.0PlatformNintendo10.858.876.482.8829.0890.00
667Wii PlayWii2006.0MiscNintendo13.839.112.932.8428.7161.64
778Duck HuntNES1984.0ShooterNintendo26.930.630.280.4728.3184.00
889New Super Mario Bros. WiiWii2009.0PlatformNintendo13.356.484.662.2526.7588.18
9910NintendogsDS2005.0SimulationNintendo9.0210.811.932.7324.5085.00
df_indexRankGame TitlePlatformYearGenrePublisherNorth AmericaEuropeJapanRest of WorldGlobalReview
189718971898Ace Combat 3: ElectrospherePS1999.0SimulationSony Computer Entertainment0.220.150.400.050.8373.92
189818981899Dynasty Warriors 2PS22000.0ActionTHQ0.240.190.340.060.8371.88
189918991900Madden NFL 07PSPNaNSportsUnknown0.770.030.000.040.8384.00
190019001901Army of Two: The 40th DayX3602010.0ShooterElectronic Arts0.520.220.000.080.8372.32
190119011902Medal of Honor: WarfighterX3602012.0ShooterElectronic Arts0.420.320.010.090.8368.00
190219021903Lizzie McGuire 2: Lizzie DiariesGBA2004.0ActionDisney Interactive Studios0.600.220.000.010.8355.00
190319031904Xenoblade ChroniclesWii2010.0Role-PlayingNintendo0.390.220.160.070.8391.74
190419041905SingStar AbbaPS32008.0MiscSony Computer Entertainment0.250.440.000.140.8373.00
190519051906FIFA Soccer World ChampionshipPS22000.0SportsElectronic Arts0.270.210.280.070.8373.00
190619061907WWE SmackDown vs. Raw 2011X3602010.0FightingTHQ0.420.320.000.090.8382.00